Wireless communications is expanding at a phenomenal rate, as more radio spectrum becomes available for commercial use and as cellular phones become commonplace. For example, in the United States, wireless phone service is offered both in the cellular (800 MHz) and PCS (1900 MHz) bands.
In addition, there is currently an evolution from analog communications to digital communications. Speech is represented by a series of bits, which are modulated and transmitted from a base station to a phone. The phone demodulates the received waveform to recover the bits, which are then converted back into speech. There is also a growing demand for data services, such as e-mail and Internet access, which require digital communications.
There are many types of digital communications systems. Traditionally, frequency-division-multiple-access (FDMA) is used to divide the spectrum up into a plurality of radio channels corresponding to different carrier frequencies. These carriers may be further divided into time slots, referred to as time-division-multiple-access (TDMA), as is done in the D-AMPS, PDC, and GSM digital cellular systems. Alternatively, if the radio channel is wide enough, multiple users can use the same channel using spread spectrum techniques and code-division-multiple-access (CDMA).
Direct-sequence (DS) spread-spectrum modulation is commonly used in CDMA systems, in which each information symbol is represented by a number of “chips.” Representing one symbol by many chips gives rise to “spreading,” as the latter typically requires more bandwidth to transmit. The sequence of chips is referred to as the spreading code or signature sequence. At the receiver, the received signal is despread using a despreading code, which is typically the conjugate of the spreading code. IS-95 and J-STD-008 are examples of DS CDMA standards.
With DS CDMA systems, coherent Rake reception is commonly used. The received signal is despread by correlating to the chip sequence, and the despread value is weighted by the conjugate of a channel coefficient estimate, removing the phase rotation of the channel and weighting the amplitude to indicate a soft or confidence value. When multipath propagation is present, the amplitude can vary dramatically. Multipath propagation can also lead to time dispersion, which causes multiple, resolvable echoes of the signal to be received. Correlators are aligned with the different echoes. Once the despread values have been weighted, they are summed. This weighting and summing operation is commonly referred to as Rake combining.
A typical digital communications system 100 is shown in FIG. 1. Digital symbols are provided to transmitter 101, which maps the symbols into a representation appropriate for the transmission medium or channel (e.g. radio channel) and couples the signal to the transmission medium via antenna 102. The transmitter signal passes through channel 103 and is received at antenna 104. The received signal is passed to receiver 105. The receiver 105 includes a radio processor 106, a baseband signal processor 110, and a post processing unit 112.
The radio processor tunes to the desired band and desired carrier frequency, then amplifies, mixes, and filters the signal down to baseband. At some point, the signal is sampled and quantized, ultimately providing a sequence of baseband received samples. Since the original radio signal has in-phase (I) and quadrature (Q) components, the baseband samples typically and I and Q components, giving rise to complex, baseband samples.
The baseband processor 110 is used to detect the digital symbols that were transmitted. It may produce soft information as well, which gives information regarding the likelihood of the detected symbol values.
The post processing unit 112 performs functions that depend highly on the particular communications application. For example, it may use the soft detected values to perform forward error correction decoding or error detection decoding. It may convert digital symbols into speech using a speech decoder.
Coherent detection requires estimation of how the symbols were modified by the transmitter, channel, and/or radio processor. As discussed previously, the transmission medium introduces phase and amplitude changes in signal, as a result of multipath propagation. The signal may also have become dispersed, giving rise to signal echoes, each echo having a phase and amplitude associated with it, represented by a complex channel coefficient. Each echo also has a delay associated with it. Coherent demodulation requires estimation of these delays and coefficients. Typically, the channel is modeled as discrete rays, with channel coefficients assigned to the different delays.
A conventional baseband processor, 200, is illustrated in FIG. 2. This is the standard baseband processor in a typical, coherent Rake receiver. The baseband signal is provided to a bank of correlators 202, which correlate different delays of the received signal to the despreading code, producing correlations, also referred to as despread values. The delays are provided by channel delay estimator 204, which uses known methods to estimate the delays, such as finding delays which give large despread values. The despread values corresponding to different delays are combined in combiner 206 using a weighted sum. The weights are the conjugates of channel coefficient estimates provided by channel coefficient estimator 208. For example, correlations to a pilot signal can be used to obtain channel coefficients.
Consider a simple example, in which the received chip-spaced baseband samples during one symbol period are represented by r(k). These samples are modeled as:r(k)=bc0s(k)+bc1s(k−1)+w(k)  (1)where b is the symbol sent, c0 and c1 are the channel coefficients, the delays are 0 and 1 chip period, s(k) is the chip sequence used to spread the symbol, and w(k) is a sequence of impairment (noise+interference) samples.
The bank of correlators produces two despread values, denoted x0 and x1, corresponding to the two rays. These can be expressed as:                               x          0                =                              1            L                    ⁢                                    ∑                              k                =                0                                            L                -                1                                      ⁢                                                   ⁢                                                            s                  *                                ⁡                                  (                  k                  )                                            ⁢                              r                ⁡                                  (                  k                  )                                                                                        (        2        )            where superscript “*” denotes complex conjugation and L is the despreading factor. Division of L is shown for illustrative purposes, while in practice it is well known how to extend results to the case when the division is omitted.                               x          1                =                              1            L                    ⁢                                    ∑                              k                =                0                                            L                -                1                                      ⁢                                                   ⁢                                                            s                  *                                ⁡                                  (                  k                  )                                            ⁢                              r                ⁡                                  (                                      k                    +                    1                                    )                                                                                        (        3        )            
The combiner combines the two despread values using estimates of the channel coefficients, denoted ĉ0 and ĉ1, to produce a detection statistic that corresponds to an information symbol. This can be expressed asz=ĉ0*x0+ĉ1*x1  (4)The symbol value that is closest to z gives the detected value {circumflex over (b)}. For BPSK modulation, b is either +1 or −1, so that the detected value is given by the sign of z.
Channel coefficients can be estimated separately using standard approaches. For example, with least mean square (LMS) estimation of c0, one would form the time varying estimate ĉ0(n), where n is an index denoting symbol period, usingĉ0(n+1)=ĉ0(n)+μ{circumflex over (b)}* (n)(x0(n)−ĉ0(n){circumflex over (b)}(n))  (5)where μ is the LMS step size. Also {circumflex over (b)} is the detected symbol value.
It can be shown that the conventional, coherent Rake receiver is optimal when the impairment samples are uncorrelated. However, for cellular communication systems, the impairment includes interference from one's own base station as well as interference from other base stations. This interference is typically noise-like at the transmitter. However, at the receiver, the interference has passed through dispersive channels, which introduce correlation. Thus, for cellular systems, the impairment samples are correlated and the conventional Rake receiver is no longer optimal, see, for example, Bottomley, “Optimizing the Rake receiver for the CDMA downlink,” Proc. 43rd IEEE Veh. Technol. Conf. (VTC '93), Secaucus, N.J., May 18-20, 1993.
Approaches which solve this problem are given in U.S. Pat. No. 5,572,552 to Dent et al. Consider combining weight formation. First IIR filtering approaches are given, in which detection statistics are formed using a weighted combination of despread values and a weighted combination of other detection statistics. Second, a FIR approach is given. Both IIR and FIR approaches rely on estimating the channel responses from each base station to the receiver as well as noise and interference power levels. This requires multiple estimation processes that increase complexity. Finally, a purely adaptive scheme is given, in which the combining weights are tracked directly using decision feedback. However, such approaches take time to converge and don't necessarily track variations well. Thus, there is a need for improved combining weight computation.
Next, consider delay estimation or correlator placement. In the aforementioned patent, an SNR criterion is used for tap placement that depends on channel response, noise power, and interference power estimates. Again, many quantities must be estimated, increasing complexity. Thus, there is a need for a lower complexity approach to correlator placement.